# -*- coding: utf-8 -*-
from datetime import timedelta
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from jms.dws.dws_wide_unsign_whole_effect_dt import jms_dws__dws_wide_unsign_whole_effect_dt

jms_dm__dm_oper_effect_sign_summary_dt = SparkSqlOperator(
    task_id='jms_dm__dm_oper_effect_sign_summary_dt',
    task_concurrency=1,
    pool_slots=2,
    master='yarn',
    execution_timeout = timedelta(minutes=30),
    email=['suning@jtexpress.com','yl_bigdata@yl-scm.com'],
    name='jms_dm__dm_oper_effect_sign_summary_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dm/dm_oper_effect_sign_summary_dt/execute.hql',
    executor_cores=4,
    executor_memory='16G',
    num_executors=10,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={
        'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
        'spark.shuffle.service.enabled' : 'true',  # 动态资源 Shuffle 服务开启
        #'spark.dynamicAllocation.maxExecutors'  : 100,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.maxExecutors'  : 27,  # 动态资源最大扩容 Executor 数
        'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
        'spark.sql.sources.partitionOverwriteMode' : 'dynamic',  # 允许删改已存在的分区
        'spark.executor.memoryOverhead'  : '2G',  # 堆外内存
        'spark.sql.shuffle.partitions'  : 600,
    },
    #hiveconf={
    #    'hive.exec.dynamic.partition': 'true',
    #    'hive.exec.dynamic.partition.mode': 'nonstrict',
    #    'hive.exec.max.dynamic.partitions.pernode': 200,
    #    'hive.exec.max.dynamic.partitions': 200
    #},
    yarn_queue='pro',
)

jms_dm__dm_oper_effect_sign_summary_dt  << [
    jms_dws__dws_wide_unsign_whole_effect_dt
]



